Import Libraries

import os
import numpy as np
import pandas as pd

import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
%matplotlib inline

from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
from sklearn.metrics import euclidean_distances
from scipy.spatial.distance import cdist

import warnings
warnings.filterwarnings("ignore")

Lire les données

Dossier contenant toutes les données

DirPath = "./data/"

data = pd.read_csv(DirPath + "data.csv")
genre_data = pd.read_csv(DirPath + "data_by_genres.csv")
year_data = pd.read_csv(DirPath + "data_by_year.csv")

Visualisation des données

data

	valence	year	acousticness	artists	danceability	duration_ms	energy	explicit	id	instrumentalness	key	liveness	loudness	mode	name	popularity	release_date	speechiness	tempo
0	0.0594	1921	0.98200	['Sergei Rachmaninoff', 'James Levine', 'Berli...	0.279	831667	0.211	0	4BJqT0PrAfrxzMOxytFOIz	0.878000	10	0.6650	-20.096	1	Piano Concerto No. 3 in D Minor, Op. 30: III. ...	4	1921	0.0366	80.954
1	0.9630	1921	0.73200	['Dennis Day']	0.819	180533	0.341	0	7xPhfUan2yNtyFG0cUWkt8	0.000000	7	0.1600	-12.441	1	Clancy Lowered the Boom	5	1921	0.4150	60.936
2	0.0394	1921	0.96100	['KHP Kridhamardawa Karaton Ngayogyakarta Hadi...	0.328	500062	0.166	0	1o6I8BglA6ylDMrIELygv1	0.913000	3	0.1010	-14.850	1	Gati Bali	5	1921	0.0339	110.339
3	0.1650	1921	0.96700	['Frank Parker']	0.275	210000	0.309	0	3ftBPsC5vPBKxYSee08FDH	0.000028	5	0.3810	-9.316	1	Danny Boy	3	1921	0.0354	100.109
4	0.2530	1921	0.95700	['Phil Regan']	0.418	166693	0.193	0	4d6HGyGT8e121BsdKmw9v6	0.000002	3	0.2290	-10.096	1	When Irish Eyes Are Smiling	2	1921	0.0380	101.665
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
170648	0.6080	2020	0.08460	['Anuel AA', 'Daddy Yankee', 'KAROL G', 'Ozuna...	0.786	301714	0.808	0	0KkIkfsLEJbrcIhYsCL7L5	0.000289	7	0.0822	-3.702	1	China	72	2020-05-29	0.0881	105.029
170649	0.7340	2020	0.20600	['Ashnikko']	0.717	150654	0.753	0	0OStKKAuXlxA0fMH54Qs6E	0.000000	7	0.1010	-6.020	1	Halloweenie III: Seven Days	68	2020-10-23	0.0605	137.936
170650	0.6370	2020	0.10100	['MAMAMOO']	0.634	211280	0.858	0	4BZXVFYCb76Q0Klojq4piV	0.000009	4	0.2580	-2.226	0	AYA	76	2020-11-03	0.0809	91.688
170651	0.1950	2020	0.00998	['Eminem']	0.671	337147	0.623	1	5SiZJoLXp3WOl3J4C8IK0d	0.000008	2	0.6430	-7.161	1	Darkness	70	2020-01-17	0.3080	75.055
170652	0.6420	2020	0.13200	['KEVVO', 'J Balvin']	0.856	189507	0.721	1	7HmnJHfs0BkFzX4x8j0hkl	0.004710	7	0.1820	-4.928	1	Billetes Azules (with J Balvin)	74	2020-10-16	0.1080	94.991

170653 rows × 19 columns

On dispose de plusieurs parametre tel que le pourcentage d'accoustique, d'instrumentale, le tempo, la dansabilité, par morceau.

genre_data

	mode	genres	acousticness	danceability	duration_ms	energy	instrumentalness	liveness	loudness	speechiness	tempo	valence	popularity	key
0	1	21st century classical	0.979333	0.162883	1.602977e+05	0.071317	0.606834	0.361600	-31.514333	0.040567	75.336500	0.103783	27.833333	6
1	1	432hz	0.494780	0.299333	1.048887e+06	0.450678	0.477762	0.131000	-16.854000	0.076817	120.285667	0.221750	52.500000	5
2	1	8-bit	0.762000	0.712000	1.151770e+05	0.818000	0.876000	0.126000	-9.180000	0.047000	133.444000	0.975000	48.000000	7
3	1	[]	0.651417	0.529093	2.328809e+05	0.419146	0.205309	0.218696	-12.288965	0.107872	112.857352	0.513604	20.859882	7
4	1	a cappella	0.676557	0.538961	1.906285e+05	0.316434	0.003003	0.172254	-12.479387	0.082851	112.110362	0.448249	45.820071	7
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2968	1	zolo	0.222625	0.547082	2.580991e+05	0.610240	0.143872	0.204206	-11.295878	0.061088	125.494919	0.596155	33.778943	9
2969	0	zouglou	0.161000	0.863000	2.063200e+05	0.909000	0.000000	0.108000	-5.985000	0.081300	119.038000	0.845000	58.000000	7
2970	1	zouk	0.263261	0.748889	3.060728e+05	0.622444	0.257227	0.089678	-10.289222	0.038778	101.965222	0.824111	46.666667	5
2971	0	zurich indie	0.993000	0.705667	1.984173e+05	0.172667	0.468633	0.179667	-11.453333	0.348667	91.278000	0.739000	0.000000	7
2972	1	zydeco	0.421038	0.629409	1.716717e+05	0.609369	0.019248	0.255877	-9.854825	0.050491	126.366087	0.808544	30.261905	7

2973 rows × 14 columns

On dispose aussi de plusieurs parametre tel que le pourcentage d'accoustique, d'instrumentale, le tempo, la dansabilité, par genre musicaux.

year_data

	mode	year	acousticness	danceability	duration_ms	energy	instrumentalness	liveness	loudness	speechiness	tempo	valence	popularity	key
0	1	1921	0.886896	0.418597	260537.166667	0.231815	0.344878	0.205710	-17.048667	0.073662	101.531493	0.379327	0.653333	2
1	1	1922	0.938592	0.482042	165469.746479	0.237815	0.434195	0.240720	-19.275282	0.116655	100.884521	0.535549	0.140845	10
2	1	1923	0.957247	0.577341	177942.362162	0.262406	0.371733	0.227462	-14.129211	0.093949	114.010730	0.625492	5.389189	0
3	1	1924	0.940200	0.549894	191046.707627	0.344347	0.581701	0.235219	-14.231343	0.092089	120.689572	0.663725	0.661017	10
4	1	1925	0.962607	0.573863	184986.924460	0.278594	0.418297	0.237668	-14.146414	0.111918	115.521921	0.621929	2.604317	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	1	2016	0.284171	0.600202	221396.510295	0.592855	0.093984	0.181170	-8.061056	0.104313	118.652630	0.431532	59.647190	0
96	1	2017	0.286099	0.612217	211115.696787	0.590421	0.097091	0.191713	-8.312630	0.110536	117.202740	0.416476	63.263554	1
97	1	2018	0.267633	0.663500	206001.007133	0.602435	0.054217	0.176326	-7.168785	0.127176	121.922308	0.447921	63.296243	1
98	1	2019	0.278299	0.644814	201024.788096	0.593224	0.077640	0.172616	-7.722192	0.121043	120.235644	0.458818	65.256542	1
99	1	2020	0.219931	0.692904	193728.397537	0.631232	0.016376	0.178535	-6.595067	0.141384	124.283129	0.501048	64.301970	1

100 rows × 14 columns

Enfin On dispose de plusieurs parametre tel que le pourcentage d'accoustique, d'instrumentale, le tempo, la dansabilité, par années.

C est pourquoi on va s'interessé a ces diffenrentes variables

La musique au fil du temps

En utilisant les données regroupées par année, nous pouvons comprendre comment la musique a changé de 1921 à 2020.

def get_date(year):
    start = int(year/10) * 10
    decenie = "{}s".format(start)
    return decenie

data["decenie"] = data["year"].apply(get_date)

sns.set(rc={"figure.figsize":(11 ,6)})
sns.countplot(data["decenie"])

<AxesSubplot:xlabel='decenie', ylabel='count'>

sound_features = ["acousticness", "danceability", "energy", "instrumentalness", "liveness", "valence"]

figure = px.line(year_data, x="year", y=sound_features)
figure.show()

A partir de 1980, les differentes variable commencent à se stabilisé à travers le temps

Musique en fonction des genres

Voici le top 10, des genres les plus écoutés

top_10_genres = genre_data.nlargest(10, "popularity")

figure = px.bar(top_10_genres, x="genres", y=["valence", "energy", "danceability", "acousticness"], barmode="group")
figure.show()

Clustering Musique avec K-Means

musique_cluster = Pipeline([("scaler", StandardScaler()), 
                                  ("kmeans", KMeans(n_clusters=20, 
                                   verbose=False))
                                 ], verbose=False)

X = data.select_dtypes(np.number)
cl = list(X.columns)
musique_cluster.fit(X)
musique_cluster_labels = musique_cluster.predict(X)
data["cluster_label"] = musique_cluster_labels

Visualisaton du Clusters avec PCA

from sklearn.decomposition import PCA

pca_pipeline = Pipeline([("scaler", StandardScaler()), ("PCA", PCA(n_components=2))])
s = pca_pipeline.fit_transform(X)
projection = pd.DataFrame(columns=["x", "y"], data=s)
projection["title"] = data["name"]
projection["cluster"] = data["cluster_label"]

figure = px.scatter(
    projection, x="x", y="y", color="cluster", hover_data=["x", "y", "title"])
figure.show()

Système de recommandation

Sur la base de l`analyse et de la visualisation, il est clair que des genres similaires ont tendance à avoir des points de données proches les uns des autres, et des types de chansons similaires sont également regroupés. On utilise cette idée pour créer un système de recommandation

import spotipy
from spotipy.oauth2 import SpotifyClientCredentials
from collections import defaultdict

#API KEY
client_id = "f69934d4ada64841b6aac8f7b69bda70"
client_secret = "584ef5b1eebc4ef1b93896f2f272e0f0"

client_manager = SpotifyClientCredentials(client_id=client_id, 
                                                      client_secret=client_secret)                                                     
sp = spotipy.Spotify(client_credentials_manager = client_manager)

def search(name, year):
    s_data = defaultdict()
    res = sp.search(q= "track: {} year: {}".format(name,year), limit=1)
    if res["tracks"]["items"] == []:
        return None

    res = res["tracks"]["items"][0]
    musique_id = res["id"]
    audio_features = sp.audio_features(musique_id)[0]

    s_data["name"] = [name]
    s_data["year"] = [year]
    s_data["explicit"] = [int(res["explicit"])]
    s_data["duration_ms"] = [res["duration_ms"]]
    s_data["popularity"] = [res["popularity"]]

    for key, value in audio_features.items():
        s_data[key] = value
    return pd.DataFrame(s_data)

from collections import defaultdict
from sklearn.metrics import euclidean_distances
from scipy.spatial.distance import cdist
import difflib

cl = ["valence", "year", "acousticness", "danceability", "duration_ms", "energy", "explicit",
 "instrumentalness", "key", "liveness", "loudness", "mode", "popularity", "speechiness", "tempo"]

def get_musique(musique, spotify_data):
    try:
        s_data = spotify_data[(spotify_data["name"] == musique["name"]) 
                                & (spotify_data["year"] == musique["year"])].iloc[0]
        return s_data
    except IndexError:
        return search(musique["name"], musique["year"])
        
def moyenne_vecteur(musique_list, spotify_data):
    musique_vectors = []
    for musique in musique_list:
        s_data = get_musique(musique, spotify_data)
        if s_data is None:
            print("n'existe pas")
            continue
        musique_vector = s_data[cl].values
        musique_vectors.append(musique_vector)  
    musique_matrix = np.array(list(musique_vectors))
    return np.mean(musique_matrix, axis=0)

def lists(dict_list):   
    dict = defaultdict()
    for key in dict_list[0].keys():
        dict[key] = []
    
    for dictionary in dict_list:
        for key, value in dictionary.items():
            dict[key].append(value)
            
    return dict

def recommandation( musique_list, spotify_data, n_musiques=15): 
    donne = ["name", "year", "artists"]
    musique_dict = lists(musique_list)
    
    musique_center = moyenne_vecteur(musique_list, spotify_data)
    scaler = musique_cluster.steps[0][1]
    scaled = scaler.transform(spotify_data[cl])
    scaled_musique= scaler.transform(musique_center.reshape(1, -1))
    distances = cdist(scaled_musique, scaled, "cosine")
    index = list(np.argsort(distances)[:, :n_musiques][0])
    
    rec_musiques = spotify_data.iloc[index]
    rec_musiques = rec_musiques[~rec_musiques["name"].isin(musique_dict["name"])]
    return rec_musiques[donne].to_dict(orient="records")

my_recommandation = recommandation(
                [{"name": "Α. Nwaar Is The New Black", "year":2017},
                {"name": "B. #QuedusaalVie", "year": 2017},
                {"name": "Baltringue", "year": 2018},
                {"name": "Δ. Dieu ne ment jamais", "year": 2017},
                {"name": "Ε. Signaler", "year": 2017}],  data)


my_recommandation

[{'name': '20 Years Later', 'year': 2018, 'artists': "['Yung Pinch']"},
 {'name': 'Virtual (Blue Balenciagas)',
  'year': 2018,
  'artists': "['YNW Melly']"},
 {'name': 'VALENTINO', 'year': 2019, 'artists': "['24kGoldn']"},
 {'name': 'My Beyoncé', 'year': 2016, 'artists': "['Lil Durk', 'DeJ Loaf']"},
 {'name': "Grass Ain't Greener", 'year': 2017, 'artists': "['Chris Brown']"},
 {'name': 'Dark Conversations', 'year': 2019, 'artists': "['Rod Wave']"},
 {'name': 'Come Closer (feat. Queen Naija)',
  'year': 2018,
  'artists': "['A Boogie Wit da Hoodie', 'Queen Naija']"},
 {'name': "Don't Wanna Breathe", 'year': 2017, 'artists': "['Kodak Black']"},
 {'name': 'Halloween', 'year': 2017, 'artists': "['Kodak Black']"},
 {'name': 'Gnarly (feat. Lil Pump)',
  'year': 2018,
  'artists': "['Kodak Black', 'Lil Pump']"},
 {'name': 'R.I.P.', 'year': 2018, 'artists': "['Playboi Carti']"},
 {'name': '911', 'year': 2018, 'artists': "['Ellise']"},
 {'name': 'Faking It (feat. Kehlani & Lil Yachty)',
  'year': 2017,
  'artists': "['Calvin Harris', 'Kehlani', 'Lil Yachty', 'Funk Wav']"},
 {'name': "Teacher's Pet", 'year': 2019, 'artists': "['Melanie Martinez']"},
 {'name': 'Eleven 11: / 11', 'year': 2014, 'artists': "['Rob Curly']"}]